3D Line Segment Generation Using Point Cloud Projection
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Solution Overview
Problem
Existing methods for generating 3D line segments from 2D images often result in false positives due to incorrect matching of 2D line segments across images, leading to inaccurate representation of real-world line segments.
Innovation Solution
Generate 3D line segments by connecting 3D points of a point cloud that correspond to points along 2D line segments in 2D images, utilizing both 2D positions of the line segments and 3D positions of the point cloud to avoid false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 2D line segments are matched across images using classic feature matching to generate 3D line segments through triangulation, then the process is simple and fast, but false positives occur leading to inaccurate representation of real-world line segments
Solution Approach 1:
The patent transitions from 2D line segment matching to 3D point cloud-based matching. By projecting 3D points onto 2D images and matching these projections across multiple views, the system leverages the additional depth dimension to disambiguate occlusions and achieve accurate 3D line segment reconstruction without false positives
Solution Approach 2:
The patent introduces a point cloud as an intermediary representation between 2D images and 3D line segments. Instead of directly matching 2D line segments, the system first converts images to a point cloud, then matches 3D points across views to generate accurate 3D line segments, using the point cloud as a mediating structure
2Productivity
If 2D line segments are used directly for triangulation without considering occlusions, then the process is efficient, but occluded objects cause false positive matches
Solution Approach 1:
The patent uses the third dimension (depth) provided by the point cloud to resolve occlusion ambiguities. By matching 3D points that are visible across multiple views and projecting them back to 2D images, the system can distinguish between truly corresponding line segments and those that appear to match due to occlusion
Solution Approach 2:
The system uses multi-view consistency as feedback to validate line segment matches. By checking whether 3D points from different views consistently project to the same 2D line segment positions, the system can reject false positive matches caused by occlusions while maintaining efficient processing
Data Source
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AI summary
Methods, storage media, and systems for generating a three-dimensional line segment are disclosed. Exemplary implementations may: receive a plurality of images; generate a point cloud based on the plurality of images; detect a two-dimensional line segment in a first image; project a set of 3d points of the plurality of 3d points as 2d points in the first image; select projected 3d points that are proximate to 2d points along the 2d line segment; and generate a 3d line segment by connecting 3d points of the point cloud represented by the selected projected 3d points.